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Critical Stress Conditions for Foam Glass Aggregate Insulation in a Flexible Pavement Layered System

2025· article· en· W4417151625 on OpenAlexafffund
Jean-Pascal Bilodeau, Erdrick Leandro Pérez-González, Di Wang, Pauline Segui

Bibliographic record

VenueInfrastructures · 2025
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of OttawaUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversité Laval
KeywordsCreepAggregate (composite)Deformation (meteorology)Stress (linguistics)ModulusRutReliability (semiconductor)

Abstract

fetched live from OpenAlex

In cold regions, flexible pavements are vulnerable to frost-induced damage, necessitating effective insulation strategies. Foam glass aggregate (FGA) insulation layers, made from recycled glass, offer promising thermal insulation properties but are mechanically fragile and susceptible to permanent deformation under repeated loading. Manufacturers provide technical recommendations, particularly regarding load limits for installation and the dimensions of the thermal protection layer. These are considered insufficient to assist pavement designers in their work. The definition of critical criteria for permissible loads was deemed necessary to design mechanically durable structures using this alternative technology. This study investigates the critical stress conditions that FGA layers can tolerate within flexible pavement systems to ensure long-term structural integrity. Laboratory cyclic triaxial tests and full-scale accelerated pavement testing using a heavy vehicle simulator were conducted to evaluate the resilient modulus and permanent deformation behavior of FGA. The results show that FGA exhibits stress-dependent elastoplastic behavior, with resilient modulus values ranging from 70 to 200 MPa. Most samples exhibited plastic creep or incremental collapse behavior, underscoring the importance of careful stress management. A strain-hardening model was calibrated using both laboratory and full-scale data, incorporating a reliability level of 95%. This study identifies critical deviatoric stress thresholds (15–25 kPa) to maintain stable deformation behavior (Range A) under realistic confining pressures. FGA performs well as a lightweight, insulating, and draining layer, but design criteria remain to be defined for the design of multi-layer road structures adapted to local materials and traffic conditions. Establishing allowable critical stress levels would help designers mechanically validate the geometry, particularly the adequacy of the overlying layers. These findings support the development of mechanistic design criteria for FGA insulation layers, ensuring their durability and optimal performance in cold climate pavements.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.296
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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